Shape classification by manifold learning in multiple observation spaces

Bridging the semantic gap for software effort estimation by hierarchical feature selection techniques

“…This process continues until one (or more) output(s) is (are) produced. The estimate models can be trained using the old training data to produce the results by fine-tuning the algorithm parameter values to reduce the difference between the actual and estimated efforts [34]. The MLP neural network in this study consisted of an input layer, a hidden layer, and an output layer.…”

“…In SDEE, both the filter [34] and wrapper [34] FS techniques have been used. Also in [35], a combination of filter and wrapper techniques have been developed.…”

“…It is necessary to mention that in feature extraction, the features of s are transformed into a different feature space, and thus there might be no correspondence between the two feature sets. The mathematical expressions and ideas underlying the feature extraction algorithms have been described in [34]. Heretofore, different FS methods have been proposed.…”

“…In feature extraction, k features were extracted from d initial features in a linear or nonlinear manner [35]. Many researchers have focused on the efficiency improvement in SDEE by reducing the sample project features [34]. Different ML algorithms have been compared in [12] to estimate the cost of the software with different datasets.…”

“…Datasets in SDEE were divided into the within-company and cross-company categories [20][21][22]. In [34], the FS effect has been investigated to SDEE within-company and crosscompany datasets, and it has been concluded that cost estimation with less features provides methods.…”